Results 21 to 30 of about 29,766 (132)

On Non‐Compact Extended Bach Solitons

Mathematische Nachrichten, EarlyView.
ABSTRACT We study the characterization of non‐compact solitons of the extended Bach flow, known as an extended Bach soliton. We prove that a weakly conformally flat extended Bach soliton (Mn,g,V)$(M^n,g,V)$ with harmonic Weyl tensor is Bach‐flat and the potential vector field V$V$ is conformal.
Rahul Poddar
wiley   +1 more source

Binary classification for imbalanced datasets using a novel metric method

Egyptian Informatics Journal
This work proposes a kernel amplification method with non-stationary characteristics for binary classification of non-noisy imbalanced datasets. Our methodology features two key innovations, including that a derived non-stationary kernel construction ...
Jian Zheng, Shengye Wang, Huyong Yan, Haichao Sun   +3 more
doaj   +1 more source

Warped products with critical Riemannian metric

Proceedings of the Japan Academy, Series A, Mathematical Sciences, 1995
Let \((B,g)\) and \((F,\overline{g})\) be two Riemannian manifolds and let \(f\) be a positive smooth function on \(B\). Then the warped product manifold \(M = B \times_f F\) is defined by the Riemannian metric \(\widetilde {g} = \pi^*(g)+ (f\circ \pi)^2 \sigma^* (\overline{g})\), where \(\pi\) and \(\sigma\) are the projections of \(B \times F\) onto \
openaire   +3 more sources

Ellipsoid‐Based Interval‐Type Uncertainty Model Updating Based on Riemannian Manifold and Gaussian Process Model

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Modern engineering systems require advanced uncertainty‐aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive‐Definite (SPD) matrix ...
Yanhe Tao, Qintao Guo, Jin Zhou, Cheng Yi   +3 more
wiley   +1 more source

Initial State Privacy of Nonlinear Systems on Riemannian Manifolds

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley   +1 more source

Development of a Framework for Geometric Analysis of Cerebral Major Arterial Centerlines

IEEE Access
Intracranial aneurysms are common cerebrovascular malformations that can lead to life-threatening cerebral hemorrhages. Abnormal hemodynamic parameters, such as wall shear stress, play a critical role in aneurysm formation. Understanding the relationship
Yan Chen, Yang Bai, Marie Oshima
doaj   +1 more source

On the analytic systole of Riemannian surfaces of finite type

, 2017
In our previous work we introduced, for a Riemannian surface $S$, the quantity $ \Lambda(S):=\inf_F\lambda_0(F)$, where $\lambda_0(F)$ denotes the first Dirichlet eigenvalue of $F$ and the infimum is taken over all compact subsurfaces $F$ of $S$ with ...
Ballmann, Werner, Matthiesen, Henrik, Mondal, Sugata   +2 more
core   +1 more source

SDFs from Unoriented Point Clouds using Neural Variational Heat Distances

Computer Graphics Forum, EarlyView.
We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from unoriented point clouds. We first compute a small time step of heat flow (middle) and then use its gradient directions to solve for a neural SDF (right). Abstract We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from ...
Samuel Weidemaier, Florine Hartwig, Josua Sassen, Sergio Conti, Mirela Ben‐Chen, Martin Rumpf   +5 more
wiley   +1 more source

Optimization of Manifold Learning Using Differential Geometry for 3D Reconstruction in Computer Vision

Mathematics
Manifold learning is a significant computer vision task used to describe high-dimensional visual data in lower-dimensional manifolds without sacrificing the intrinsic structural properties required for 3D reconstruction. Isomap, Locally Linear Embedding (
Yawen Wang
doaj   +1 more source

EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models

Computer Graphics Forum, EarlyView.
EvolvED visualises how diffusion models generate images by embedding intermediate outputs to preserve semantics and evolutionary structure. It supports analysis via (a) user‐defined goals and prompts, (b) sampling intermediate images, (c) extracting relevant features, and (d) visualising them in structured radial and rectilinear layouts for ...
Vidya Prasad, Hans van Gorp, Christina Humer, Ruud J. G. van Sloun, Anna Vilanova, Nicola Pezzotti   +5 more
wiley   +1 more source